Biological Dynamics & Networks

CHE 559 / PHY 559 / AMS 591

Fall 2013 T/Th 2:30-3:50 pm in Laufer Center 101

Tom MacCarthy, Course PI

Guest lecturers: Ken Dill, Sergei Maslov, Jin Wang, Jack Peterson

This course will provide a solid foundation in key theoretical concepts for the study of dynamics in biological systems and networks at different scales ranging from the molecular level to metabolic and gene regulatory networks.


Reference Books

  • Ken Dill, Molecular Driving Forces: Statistical Thermodynamics in Biology, Chemistry, Physics, and Nanoscience
  • Bernhard Palsson, Systems Biology: simulation of dynamic network states
  • Eberhard Voit, A first course in Systems Biology
  • Uri Alon, An introduction to Systems Biology
  • M.E.J. Newman, Networks: an introduction

 

Dates Topics Reading Speakers
8/28, 8/30 Introduction to networks and statistical thermodynamics
  1. Brief introduction to networks in biology and beyond
  2. Physical kinetics
  3. Diffusion, Smoluchowski
  4. Random flights
MacCarthy, Dill
9/4, 9/6 Statistical thermodynamics II
  1. Waiting times
  2. Brownian ratchets
  3. Chemical kinetics
  4. Transition states
Dill
9/11, 9/13 Biochemical networks
  1. Rate laws and basic properties of reactions
  2. Reversible linear and bilinear reactions
  3. Connected reversible linear and bilinear reactions
  4. Autocatalysis and dynamical stability
Palsson, Chap 2 and 4 MacCarthy, Wang
9/18, 9/20 Enzyme kinetics
  1. Background on enzyme catalysis
  2. Michaelis-Menten kinetics
  3. Hill kinetics for enzyme regulation
  4. Cooperative phenomena
Palsson, Chap 5 MacCarthy
9/25, 9/27 Network measurements
  1. Networks as graphs
  2. Non-biological networks: technological, social and information networks
  3. Degree distribution
  4. Centrality measures
Newman, Chap 6 and 7 MacCarthy
10/2, 10/4 Large-scale structure of networks
  1. The small-world effect
  2. Power laws and scale-free networks
  3. Clustering coefficients
Newman, Chap 8 Maslov
10/9, 10/11 Network evolution models
  1. Properties of random graphs
  2. Preferential attachment models
Newman, Chap 12 and 14 Peterson
10/16, 10/18 Metabolic networks I
  1. Background on metabolism
  2. Modeling large systems using stoichiometric networks
  3. Case study: glycolysis
Palsson, Chap 7 and 10 MacCarthy
10/23, 10/25 Metabolic networks II
  1. Metabolomics
  2. Metabolic network reconstruction
  3. Flux analysis
Voit, Chap 3 and 8 MacCarthy
10/30, 11/1 Gene regulatory networks I
  1. Background on gene regulation and transcription networks
  2. Network motifs
  3. Biological oscillators and autoregulation
Alon, Chap 2-4 MacCarthy, Wang
11/6, 11/8 Signal transduction systems
  1. Background on signal transduction
  2. Two-component signaling systems
  3. Bistability and hysteresis
Voit, Chap 9 MacCarthy
11/13, 11/15, 11/20 Robustness
  1. Overview of biological robustness
  2. Robustness in signalling networks: bacterial chemotaxis
  3. Robust patterning in development
Alon, Chap 7 and 8 MacCarthy
11/22 Thanksgiving break
11/27, 11/29 Algorithms for network analysis
  1. Modularity in biology
  2. Community detection
Newman, Chap 11 MacCarthy
12/4, 12/6 Modeling noise
  1. Definitions of intrinsic and extrinsic noise
  2. Case study: M.B. Elowitz et al., 2002, Stochastic gene expression in a single cell, Science, 297, 1183-1186.
  3. Gillespie algorithm
Wang